scholarly journals Sustainable Management of a Matured Oil Palm Plantation in UPM Campus, Malaysia Using Airborne Remote Sensing

2009 ◽  
Vol 2 (3) ◽  
Author(s):  
Kamaruzaman Jusoff
Keyword(s):  
Remote Sensing ◽  
Oil Palm ◽  
Sustainable Management ◽  
Airborne Remote Sensing ◽  
Oil Palm Plantation

scholarly journals Analysis of Forest Function Transfer to Oil Palm Plantation in Parenggean District with the Remote Sensing Approach and Geographic Information System

2019 ◽  
Vol 3 (2) ◽  
pp. 217-223
Author(s):  
Marboles Kundrat ◽  
Frederik Samuel Papilaya
Keyword(s):  
Remote Sensing ◽  
Oil Palm ◽  
Systems Approach ◽  
Research Area ◽  
Geographic Information ◽  
Forest Conversion ◽  
Oil Palm Plantation ◽  
Forest Land Cover ◽  
Forest Function ◽  
Function Transfer

The island of Kalimantan is one of the islands that has a vast forest. Kalimantan Island is also the most important island for Indonesia, even the world. Parenggean is one of the sub-districts located in Kotawaringin Timur Regency, Central Kalimantan Province. Parenggean sub-district with an area of 493.15 km² is one of the sub-districts in East Kotawaringin Regency which has a very large oil palm plantation. This study will present data on the amount of forest land cover that has been converted. To get extensive forest conversion, this research uses the Remote Sensing and Geographic Information Systems approach. The result of research this proves there have been over the function forests became oil palm plantation in Parenggean District. The area of ​​forest that was converted into oil palm plantation in the research area is 5,143.15 hectares in 1990-2000 and 17,560.45 hectares in 2000-2010.  

Oil palm plantation mapping from high-resolution remote sensing images using deep learning

2019 ◽  
Vol 41 (5) ◽  
pp. 2022-2046 ◽  
Author(s):  
Runmin Dong ◽  
Weijia Li ◽  
Haohuan Fu ◽  
Lin Gan ◽  
Le Yu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Oil Palm ◽  
Remote Sensing Images ◽  
Oil Palm Plantation

scholarly journals Economic Potential of Oil Palm Plantation Using Remote Sensing-Based Technology in Indonesia

2021 ◽  
Vol 14 (1) ◽  
pp. 19-34
Author(s):  
Shinta Rahma Diana ◽  
◽  
Farida Farida ◽  
Keyword(s):  
Remote Sensing ◽  
Palm Oil ◽  
Oil Palm ◽  
Quantitative Methods ◽  
Multinomial Logistic Regression ◽  
Secondary Data ◽  
Multiple Regression Model ◽  
Economic Potential ◽  
Oil Palm Plantation ◽  
Sensing Model

Abstract. The productivity of Indonesia's palm oil was considered low when referring to the 14.6 million ha land area in 2019, with the production of national palm oil only reaching 3.2 tons of CPO/ha/year. The uses of remote sensing technology as a means of monitoring and supervising, were expected to increase oil palm production in line with productivity. The purpose of this study was to determine the economic potential based on oil palm plantation productivity, with and without using remote sensing-based technology, as well as other variables likely to affect productivity. Primary and secondary data collection methods were also used in this research. There were three quantitative methods being used in this study, namely (i) Multiple regression model with panel data, (ii) Data Envelopment Analysis (DEA) tool, and (iii) Multinomial logistic regression technique. The results showed that the generated economic potential from the utilization of the remote sensing model, had efficient opportunity value of 10.48, which was higher than the non-usage of the technology. Therefore, the main variables that affected productivity in this study, were fertilizer and labour. Keywords: Efficiency, oil-palm, remote sensing (spot 6), policy, binomial logistic

scholarly journals A map of the extent and year of detection of oil palm plantations in Indonesia, Malaysia and Thailand

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Olga Danylo ◽  
Johannes Pirker ◽  
Guido Lemoine ◽  
Guido Ceccherini ◽  
Linda See ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Southeast Asia ◽  
Oil Palm ◽  
Abrupt Increase ◽  
The Past ◽  
Oil Palm Plantation ◽  
Time Series Approach ◽  
Landscape Level

AbstractIn recent decades, global oil palm production has shown an abrupt increase, with almost 90% produced in Southeast Asia alone. To understand trends in oil palm plantation expansion and for landscape-level planning, accurate maps are needed. Although different oil palm maps have been produced using remote sensing in the past, here we use Sentinel 1 imagery to generate an oil palm plantation map for Indonesia, Malaysia and Thailand for the year 2017. In addition to location, the age of the oil palm plantation is critical for calculating yields. Here we have used a Landsat time series approach to determine the year in which the oil palm plantations are first detected, at which point they are 2 to 3 years of age. From this, the approximate age of the oil palm plantation in 2017 can be derived.

Towards global oil palm plantation mapping using remote-sensing data

2018 ◽  
Vol 39 (18) ◽  
pp. 5891-5906 ◽  
Author(s):  
Yuqi Cheng ◽  
Le Yu ◽  
Yidi Xu ◽  
Xiaoxuan Liu ◽  
Hui Lu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Oil Palm ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Oil Palm Plantation

scholarly journals Pengamatan dan Pemetaan Penyakit Busuk Pangkal Batang di Perkebunan Kelapa Sawit Menggunakan Unmanned Aerial Vehicle (UAV) dan Kamera Multispektral

2020 ◽  
Vol 16 (2) ◽  
pp. 69-80
Author(s):  
Heri Santoso
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Satellite Imagery ◽  
Oil Palm ◽  
Cloud Cover ◽  
Stem Rot ◽  
Basal Stem Rot ◽  
Oil Palm Plantation ◽  
Palm Trees ◽  
Aerial Vehicle

Surveillance and Mapping of Basal Stem Rot Disease in Oil Palm Plantation Using Unmanned Aerial Vehicle (UAV) and Multispectral Camera Basal stem rot (BSR) disease caused by Ganoderma boninensis is still a major disease in oil palm plantations both in Indonesia and Malaysia. In some countries, remote sensing approach has been used for monitoring BSR in oil palm plantation. However, the utilization of satellite imagery in remote sensing especially in vegetation study on the tropical region was often limited by cloud cover. A drone or unmanned aerial vehicle (UAV) utilization is the best way to deal with cloud cover in the tropic region. Machine learning of random forest (RF) and satellite imagery used in the BSR study produced good accuracy. This research was aimed to identify and monitor the BSR infection on individual oil palm trees using an UAV and multispectral camera and RF classification. The results showed that the data acquired from UAV was affected by cloud shadows. The RF classification of healthy and infected oil palm trees by BSR disease and the spreading map of BSR infection was affected by cloud shadows. The highest accuracy of healthy and infected oil palm by BSR was 79.49%. Reflectance calibrator, digital to reflectance conversion, and model implications to build spreading map of BSR infection need to be conducted both on the clear area and the cloud shadow-covered area. Moreover, the UAV-based data should be considering the cloud view on the coverage area.

DEVELOPMENT OF RAPID LOW-COST LARS PLATFORM FOR OIL PALM PLANTATION

2015 ◽  
Vol 77 (20) ◽  
Author(s):  
Nasruddin Abu Sari ◽  
A Ahmad ◽  
MY Abu Sari ◽  
S Sahib ◽  
AW Rasib
Keyword(s):  
Remote Sensing ◽  
Oil Palm ◽  
Precision Agriculture ◽  
Low Cost ◽  
Digital Cameras ◽  
Large Area ◽  
Oil Palm Plantation ◽  
Personal Remote Sensing ◽  
Path Distance ◽  
Low Altitude

The need to produce high temporal remote sensing imagery for supporting precision agriculture in oil palm deserves a new low-altitude remote sensing (LARS) technique. Consumer over the shelf unmanned aerial vehicles (UAV) and digital cameras have the potential to serve as Personal Remote Sensing Toolkits which are low-cost, efficient, rapid and safe. The objectives of this study were to develop and test a new technique to rapidly capturing nadir images of large area oil palm plantation (1 km2 ~ 4 km2). Using 5 different multi-rotor UAV models several imagery missions were carried out. Multi-rotors were chosen as a platform due to its vertical take-off and landing (VTOL) feature. Multi-rotor’s VTOL was crucial for imagery mission success. Post processing results showed that for an area of 1 km2, it needs 2 to 6 sorties of quad-rotor UAV with 4000x3000 pixel digital cameras flying at altitude of 120m above ground level and an average of 50m cross-path distance. The results provide a suitability assessment of low-cost digital aerial imagery acquisition system. The study has successfully developed a decent workhorse quad-rotor UAV for Rapid Aerial Photogrammetry Imagery and Delivery (RAPID) in oil palm terrain. Finally we proposed the workhorse UAV as Low-Altitude Personal Remote Sensing (LAPERS) basic founding element.

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